AI Product Management Insights

The role of product management, especially for AI-based products, is changing a lot. Interestingly, a significant number of products are becoming "AI-based" products. You'll often see requests for a stronger technical background alongside traditional PM skills. It's not enough to just know the market and users anymore; product managers now need to understand things like algorithms, data pipelines, and machine learning. This isn't a small change; it's a real shift in what's required. It’s not about knowledge of a toll but the technology. I'm seeing this trend firsthand. Look at product manager job descriptions, and "understanding or working knowledge of AI" is becoming standard. We're also seeing more data scientists and AI engineers moving into product management. This isn't just a career switch; it's a sign that technical knowledge is crucial for building good AI products. For people without this background, it's a big challenge, requiring a lot of learning and a willingness to try new things. Being able to explain complex technical ideas in a way that users understand is now a must-have skill. The key to AI product management is balancing big ideas with what's actually possible. Without understanding AI's strengths and limitations, product managers can easily get swayed by marketing hype or struggle to create realistic roadmaps. It's the difference between a dream and a practical vision. Equally important is building strong communication with engineering teams, not just for technical alignment but for building trust. Don't believe the idea that you don't need technical skills in PM. This trend is only going to get stronger. It's better to adapt and learn than to struggle later. #ExperienceFromTheField #WrittenByHuman

Everyone says the future of product management is AI-native. But what the hell does it mean to be an AI-native PM? After watching our instructors teach thousands of students at Maven and observing my own team's transformation, I think it comes down to two layers. 1. The technical layer If you want to build AI-first products, you need to know how they work.   • AI fundamentals. What an LLM actually is, the trade-offs of using something like RAG, when to use agents (one or multiple), and what evals are. You need to speak the language fluently enough to collaborate with engineers without a translator.   • Model intuition and selection. When to fine-tune, how cost and intelligence scales with model size.   • AI product sense. AI products have fundamentally different requirements. A mediocre AI experience is worse than no AI experience at all. You need to understand guardrails, failure modes, and how to design for non-determinism. 2. The productivity layer PMs should use AI as a second nature part of their day-to-day work. For existing PMs, this requires shifting their workflows entirely...   • Prototyping. Instead of PRDs, start by using tools like Cursor or Claude Code to ship and iterate on prototypes and feature demos.   • Research and insights. Use LLMs to synthesize data of all types (not just CSVs) into usable insights. Read the original data to ensure accuracy and deeply understand the context the LLM is presenting.   • Strategy and writing. You still do your own thinking, while leveraging AI to fill in the gaps. AI can produce excellent docs and decompose them into tasks given enough context and prompting, but it shouldn't make the final decisions.   • Personal software. Use tools like Claude to build small apps and tools that only you use, optimized entirely for your specific workflows and use cases. Taste and judgement still matter the same as they did before. PMs are still expected to be the CEO of their products. But they also need to be natively using AI in their work, and deeply understand the opportunities to build AI-driven products. P.S. BTW we’re partnering with Lenny Rachitsky to launch a new series of free lessons called “The AI-Native Product Manager”. Check it out: https://bit.ly/4s0mYYj   • The CTO of MySpace turned ML Product Lead at Google, Dmitry Shapiro, on how to best use Clawdbot as a PM   • The 1st Product Manager, v0 at Vercel, Ary Khandelwal, on how PMs can build and *deploy* code with no handoff   • Ex-Head of UXR, Spotify Business, Caitlin Sullivan, on when and how to construct synthetic data for product discovery   • The former CPO of LinkedIn, Tomer Cohen, on becoming a full stack builder with AI   • Former Director of Growth at Gitlab, Hila Qu 曲卉, on the The AI-powered VP of Growth playbook   • Former FDE Lead at Palantir and Citadel, Vinoo Ganesh, on building products like a forward deployed engineer   • Product Lead at Roblox, Peter Yang, on AI Powered Product Skills for Executive Leaders & GMs

If you're a product manager and not using generative AI yet… you're falling behind. Over the past few months, I’ve been exploring how PMs across industries are adopting AI - not for hype, but to actually get things done faster and smarter. I got inputs from 300+ product managers and here’s what real product managers are using generative AI for: ↳ Summarizing customer feedback from surveys and reviews ↳ Writing better PRDs, FAQs, and user stories (yes, even from Figma screens!) ↳ Brainstorming product ideas and outlining go-to-market strategies ↳ Automating SQL queries and documentation ↳ Creating wireframes, mockups, and prototypes in minutes ↳ Preparing pitch decks, emails, and product update announcements ↳ Synthesizing competitor analysis and market research ↳ Managing team workflows and Slack/Notion chaos with AI agents From ChatGPT and Claude to Notion AI, Cursor, and Replit - PMs are building powerful workflows around AI. Some have even built their own agents for writing specs or organizing roadmap inputs. The goal? Free up time for deep thinking and high-impact decisions. This isn't about replacing PMs. It's about amplifying what we do best: understanding users, aligning teams, and shipping value. If you’re just starting out, begin small: ↳ Ask AI to rewrite an email ↳ Summarize a user interview ↳ Draft a product update from bullet points You’ll be surprised how quickly it becomes your second brain. Are you already using AI in your product workflow? Follow Lokesh Gupta for more such insights.

AI won't replace product managers. It will make the bar much higher. Last week's Product Thinking podcast brought together some incredible voices like Jessica Hall (CPO at Just Eat Takeaway.com), Mario Rodriguez (CPO at GitHub), Steve Wilson (CPO at Exabeam), and Darren Wilson (CPO at Soul Machines), among others. But one insight from Anthony Maggio (VP Product Management at Airtable) got me thinking about the future of Product Management. "AI is actually going to increase the expectations of the PM function. There's a lot of things that PMs do that AI is actually already quite good at. Taking data and analysis from many different sources and using that to craft strategy and set goals or write PRDs." Here's the paradox: as AI handles the tactical work, expectations for strategic thinking will skyrocket. Right now, many PMs spend hours collecting data, writing PRDs, and synthesizing basic market research. That's time not spent on deep customer insights, competitive intelligence, or identifying emerging opportunities. When AI can pull together market data and draft documents in minutes, what separates good PMs from great ones? The ability to read between the lines, spot patterns others miss, and make strategic bets that aren't obvious from the data alone. As Anthony put it, "historically there have been so many inputs that it is difficult for PMs to stay on top of all of those things proactively." AI removes that excuse. The PMs who thrive will use this shift to become true strategic thinkers, not just feature managers. How are you preparing for higher expectations in your PM role?

𝗔𝗜 𝗠𝗮𝗸𝗲𝘀 𝗚𝗿𝗲𝗮𝘁 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝗠𝗮𝗻𝗮𝗴𝗲𝗿𝘀 𝗚𝗿𝗲𝗮𝘁𝗲𝗿 - But It Won’t Save Poor Thinking AI won’t make you a better product manager. It 𝗮𝗺𝗽𝗹𝗶𝗳𝗶𝗲𝘀 the skills you already have—or don’t. A great PM doesn’t start with prompts. They start with 𝗰𝗹𝗮𝗿𝗶𝘁𝘆: a real problem, a business need, and the thinking to connect the dots. But here’s the good news: If you’re already strategic, AI can make you 𝗳𝗮𝘀𝘁𝗲𝗿, 𝘀𝗵𝗮𝗿𝗽𝗲𝗿, 𝗮𝗻𝗱 𝗺𝗼𝗿𝗲 𝗲𝗳𝗳𝗲𝗰𝘁𝗶𝘃𝗲. Here are 𝟱 𝘄𝗮𝘆𝘀 𝗴𝗿𝗲𝗮𝘁 𝗣𝗠𝘀 𝗮𝗿𝗲 𝘂𝘀𝗶𝗻𝗴 𝗔𝗜 𝗿𝗶𝗴𝗵𝘁 𝗻𝗼𝘄—and how you can too: 1. 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗳𝗮𝘀𝘁𝗲𝗿 & 𝗱𝗲𝗲𝗽𝗲𝗿.    Great PMs understand their market →    Use AI to summarize earnings calls, analyze reviews, extract competitor positioning, or generate trend reports across industries in seconds.     2. 𝗕𝘂𝗶𝗹𝗱 𝘀𝘁𝗿𝗼𝗻𝗴𝗲𝗿 𝗳𝗶𝗻𝗮𝗻𝗰𝗶𝗮𝗹 𝗳𝗹𝘂𝗲𝗻𝗰𝘆.    Great PMs think like CFOs →    Use AI to break down unit economics, simulate pricing models, run revenue impact scenarios, or benchmark competitor pricing.     3. 𝗩𝗮𝗹𝗶𝗱𝗮𝘁𝗲 𝗵𝘆𝗽𝗼𝘁𝗵𝗲𝘀𝗲𝘀 𝗺𝗼𝗿𝗲 𝗲𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝘁𝗹𝘆.    Great PMs don’t guess - they test →    Use AI to quickly draft multiple positioning statements, survey questions, or user interview scripts.    Ask AI: “𝘞𝘩𝘢𝘵 𝘢𝘴𝘴𝘶𝘮𝘱𝘵𝘪𝘰𝘯𝘴 𝘢𝘳𝘦 𝘸𝘦 𝘮𝘢𝘬𝘪𝘯𝘨—𝘢𝘯𝘥 𝘩𝘰𝘸 𝘤𝘢𝘯 𝘸𝘦 𝘵𝘦𝘴𝘵 𝘵𝘩𝘦𝘮?”     4. 𝗥𝗲𝗰𝗼𝗴𝗻𝗶𝘇𝗲 𝗽𝗮𝘁𝘁𝗲𝗿𝗻𝘀 𝘁𝗵𝗮𝘁 𝗱𝗿𝗶𝘃𝗲 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝘆.    Great PMs spot signals early →    Use AI to synthesize internal feedback, sales calls, support tickets, and roadmap themes to surface patterns others miss.     5. 𝗣𝗿𝗼𝘁𝗼𝘁𝘆𝗽𝗲 & 𝗰𝗼𝗺𝗺𝘂𝗻𝗶𝗰𝗮𝘁𝗲 𝗮𝘁 𝗹𝗶𝗴𝗵𝘁𝗻𝗶𝗻𝗴 𝘀𝗽𝗲𝗲𝗱.    Great PMs move ideas forward →    Use AI to generate mockups, create product briefs, or prep storytelling decks that get stakeholder buy-in faster.     AI won’t teach you product thinking. But if you’re already building that muscle, it will take you from good → great → unstoppable. 👇 Which of these are you already using - and what would you add? #ProductManagement  #StrategicThinking

Do you want to become an AI PM… or just the manager of an AI feature factory? Last quarter, I almost shipped the wrong AI product. Engineering loved it. The model worked. Accuracy looked great. The demo impressed leadership. Momentum was building fast. But not questioning why fast? And that’s exactly when Product Management becomes critical. --- Everyone in the room had a valid perspective: Engineering saw innovation. Design saw elegance. Business saw efficiency and cost savings. But one question was missing: 👉Why does this need to exist for the customer? AI creates a dangerous illusion — when technology becomes powerful, teams stop discovering problems and start justifying solutions. 💪 So I paused the launch. Not to fix execution. To rediscover intent. --- I went back to customers. Not dashboards. Not surveys. Conversations. And the real problem emerged. Customers weren’t struggling with effort. They were struggling with risk. They didn’t want AI making decisions for them. They wanted confidence that their decisions wouldn’t fail. They weren’t buying automation. They were buying trust. --- We changed direction. Instead of building AI that decided, we built AI that explained. Same technology. Completely different outcome. Adoption accelerated almost immediately. Because great AI products don’t start with models. They start with human psychology balanced with company P&L reality. --- This is the part of Product Management people rarely talk about. The role isn’t shipping shiny AI features. It’s preventing companies from confidently building the wrong thing. Engineering expands possibility. Design shapes interaction. Business protects revenue. Product defines meaning. AI is quietly dividing PMs into two groups: → Those who discover why before deciding what. → Those who run AI feature factories. Only one builds strategy. 👉 Choose Wisely #ProductManagement #AILeadership #ProductStrategy --- What’s the closest you’ve seen a team come to launching something technically brilliant… but completely unnecessary?

While building Planbow, I realized that a product manager needs market insights more than marketing and sales teams, and that’s the biggest reason a modern PM should be equipped with AI superpowers. Let’s understand why: Matching the speed of development- As we are seeing development co-pilots, low-code and no-code tools are ready with their disruptive capabilities and now building software is possible in weeks. Matching this agility with conventional product management will become the bottle-neck. Data-Driven Decisions- A product manager needs to make decisions based on ever-changing market dynamics, customer behavior, and competitor strategies. AI helps in gathering and analyzing vast amounts of data quickly, providing actionable insights that go beyond traditional research methods. Predicting Trends- AI can analyze historical data and predict future trends, enabling product managers to stay ahead of the curve. This is crucial for crafting features and strategies that resonate with future market needs, not just current demands. Customer Insights- Understanding customer pain points and preferences is key to successful product development. AI-powered tools can analyze customer feedback, reviews, and behavior in real-time, helping PMs refine the product roadmap. Efficiency in Execution- AI can automate repetitive tasks like A/B testing, performance tracking, and even certain design decisions, allowing product managers to focus on strategic initiatives that drive growth. Personalization- In today’s competitive landscape, personalization is everything. AI allows product managers to create highly personalized user experiences based on data, ensuring that the product remains relevant to diverse user segments. In short, AI empowers product managers to make smarter, faster, and more precise decisions, ensuring that their product stays competitive and innovative in a constantly evolving market.

🚀 Building AI Products in 2024: Key Takeaways for PMs & Founders 🚀 Yesterday, I had the privilege of presenting some of my insights in building AI Products at PM exercises forum 🎤, drawing from the experience while helping 10+ early-stage AI startups and building one myself. 🧠 Here are some critical lessons I shared that can help AI founders and Product managers: 1️⃣ Every PM is now an AI PM 🤖 AI fluency isn't optional—it's essential. Adopt an AI-first mindset, reimagining product experiences from the ground up. 🔄 2️⃣ Product Fundamentals Remain 👑, But the Stakes Are Higher 🎯 User obsession is key. 👥 AI amplifies the impact of both good 👍 and bad 👎 decisions—understand your users deeply. 3️⃣ Don’t Build an LLM on Day One 🛠️ Start with existing models and APIs. 🧩 Rapid experimentation beats premature optimization. ⏱️ 4️⃣ It’s Not About the Features, It’s About the Transformation ✨ AI should revolutionize workflows and create disruptive value, not just add features. 🚀 5️⃣ Accuracy, Reliability, and Speed Matter ⏱️✅ Users won’t tolerate errors, delays, or inconsistencies. Strive for excellence from the start. 🥇 6️⃣ Don’t Worry Too Early About Token Cost Optimization 💰 Focus on nailing the value proposition first. Optimize costs once you have traction. 📈 7️⃣ Data is Your Goldmine 💎 Quality over quantity—invest in clean, well-structured data. Garbage in, garbage out. 🗑️➡️🗑️ 8️⃣ Embrace Experimentation & Iteration, with Purpose & Flexibility 🧪🔄 Fail fast, learn faster, but ensure each experiment has clear hypotheses and metrics. 🎯 Build adaptable systems that can evolve with AI advancements. 🌱 9️⃣ Learn the Tech, But Don’t Get Lost in the Jargon 📚 Understand both the "how" and the "when" of tools like fine-tuning and prompt engineering. 🤔 🔟 Delightful UX is Key to Adoption ❤️ AI shouldn’t feel alien—integrate it seamlessly into the user experience. 🤝 1️⃣1️⃣ Human-in-the-Loop is Essential 🧑🤝🧑 AI is a tool, not a replacement. Human oversight ensures accuracy and accountability. ⚖️ 1️⃣2️⃣ Watch Out for Hallucinations & Bias 🧐 Implement checks to catch AI errors and actively work to identify and mitigate bias. 🕵️♀️ 1️⃣3️⃣ Build for the Long Term, But Start Today 🏁 AI is a marathon, not a sprint. The longer you wait, the further behind you'll be. 🏃♀️ 1️⃣4️⃣ Measure, Learn, and Adapt Continuously ♻️ Set clear KPIs, iterate based on user feedback, and embrace continuous improvement. 📈 These insights are grounded in real-world experience. Goal is to help you sidestep the mistakes I’ve seen and help you to lead building AI products. 💪 AI isn’t just a technological shift—it’s a paradigm shift. As PMs and founders, it’s time to step up, think big, and build the future. 🚀 #AI #ProductManagement #Innovation #TechLeadership #AIProducts #Startups P.S. If you're a PM navigating the AI landscape, feel free to connect and share your experiences! Let's learn and grow together. 🌱

🚀 The craft of product management is changing. Fast. A few days ago, I sat down with Vimarsh Puneet. What started as a quick 5-minute chat turned into one of the most eye-opening demos I’ve seen in a while. Vimarsh showed me how AI is reshaping the day-to-day work of a PM — not by adding more tools, but by helping us think and build differently. Imagine this 👇 You describe the intent of a project — say, “I want to analyze customer pain points, personas, and competitive solutions for an on-prem AI agent.” In seconds, an AI agent spins up an entire directory structure with: - competitive analysis, - segmentation, - customer data sheets — all in markdown, ready for you to iterate with. Need to analyze adoption trends or update features in Aha!? The agent does it. Want to ideate on next steps? You can literally have a conversation with your tool. It’s VS Code + Copilot + Claude Sonnet 4 + MCP integration, all working together to 10x what a PM can do. What struck me most wasn’t the automation — it was the mindset shift. PMs aren’t just writing specs anymore. They’re writing intent — and the AI builds the spec. This is what I mean when I talk about bringing a founder mindset into product management. A future where PMs lead small “teams” of AI agents, each contributing analysis, insights, and ideas — so we can focus on vision, creativity, and customer empathy. If you’re a PM today, this is your moment to reimagine your craft. AI isn’t replacing what we do — it’s expanding how far we can go. 🎥 Here’s the full 5-minute conversation with Vimarsh.

I think Product Management has changed more in the last year than in the previous 10 years combined. Tasks that used to take hours and even days can now be done in minutes. Or even completely automated. Here are a few real world examples in our little team - 1) For customer feedback, the team has been using GitHub Copilot in agent mode against feedback datasets to analyze feedback at scale—getting to insights in minutes that used to take hours of manual KQL and verbatim reading. 2) On prototyping, Claude Code and the Figma MCP have made it possible to go from concept to interactive prototype without lengthy spec handoffs, with one key finding along the way: describing the user experience you want produces far better AI-generated code than describing the implementation. 3) On bug fixing, Copilot in VS Code and AzureDevOps has enabled the team to take bugs or UX tweaks that surface in meetings and turn  them into working PRs the same day—without pulling an engineer off their work. 4) And on collaboration, the team has been experimenting with AI-native prototype-first working environments where prompts, PRDs, and technical specs can be generated and iterated in real time across PM, Design, and Engineering. They are not just "AI projects" anymore. They are part of the core PM workflow now. Just like writing a .docx PRD was in the past.